In this paper, we introduce a divide-and-conquer algorithm to improve
sentence alignment speed. We utilize external bilingual sentence embeddings to
find accurate hard delimiters for the parallel texts to be aligned. We use
Monte Carlo simulation to show experimentally that using this
divide-and-conquer algorithm, we can turn any quadratic time complexity
sentence alignment algorithm into an algorithm with average time complexity of
O(NlogN). On a standard OCR-generated dataset, our method improves the
Bleualign baseline by 3 F1 points. Besides, when computational resources are
restricted, our algorithm is faster than Vecalign in practice.

本研究介绍了一种分治算法，利用外部双语句子嵌入来找到精确的边界，将任何二次时间复杂度的句子对齐算法转化为平均时间复杂度为 O (NlogN) 的算法，并在标准 OCR 生成的数据集上将 Bleualign 基线提高了 3 个 F1 点。

分治法提高句子对齐精度

Improve Sentence Alignment by Divide-and-conquer

We propose a novel model architecture and training algorithm to learn
bilingual sentence embeddings from a combination of parallel and monolingual
data. Our method connects autoencoding and neural machine translation to force
the source and target sentence embeddings to share the same space without the
help of a pivot language or an additional transformation. We train a multilayer
perceptron on top of the sentence embeddings to extract good bilingual sentence
pairs from nonparallel or noisy parallel data. Our approach shows promising
performance on sentence alignment recovery and the WMT 2018 parallel corpus
filtering tasks with only a single model.

本文提出一种新型模型架构及培训算法，可从平行和单语数据的组合中学习双语句子嵌入。我们通过自编码和神经机器翻译将源语言和目标语言的句子嵌入连接起来。并通过多层感知机来提取非平行或有噪声的双语句对，此方法在句子对齐和 WMT2018 上表现出有希望的性能。

通过自编码学习双语句子嵌入并使用多层感知器计算相似度

Learning Bilingual Sentence Embeddings via Autoencoding and Computing  Similarities with a Multilayer Perceptron

This paper presents an effective approach for parallel corpus mining using
bilingual sentence embeddings. Our embedding models are trained to produce
similar representations exclusively for bilingual sentence pairs that are
translations of each other. This is achieved using a novel training method that
introduces hard negatives consisting of sentences that are not translations but
that have some degree of semantic similarity. The quality of the resulting
embeddings are evaluated on parallel corpus reconstruction and by assessing
machine translation systems trained on gold vs. mined sentence pairs. We find
that the sentence embeddings can be used to reconstruct the United Nations
Parallel Corpus at the sentence level with a precision of 48.9% for en-fr and
54.9% for en-es. When adapted to document level matching, we achieve a parallel
document matching accuracy that is comparable to the significantly more
computationally intensive approach of [Jakob 2010]. Using reconstructed
parallel data, we are able to train NMT models that perform nearly as well as
models trained on the original data (within 1-2 BLEU).

该研究提出了一种有效的并行语料库挖掘方法，使用双语句子嵌入进行训练，通过引入硬负例来实现。该方法是基于语义相似度的，结果表明该方法可以用于重建平行文本，从而训练出 NMT 模型，与使用原始数据训练的模型相差不大。